Camera-Based Sleep State Detection From Respiratory Waveforms
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Solution Overview
Problem
Existing systems for determining sleep states, such as those using wired probes, are intrusive and inefficient, particularly for monitoring sleep quality and detecting respiratory events during sleep cycles.
Innovation Solution
A non-contact system using a depth-sensing camera to generate respiratory waveforms, which are labeled with EEG-based sleep state classifications, and trained with a machine learning model to predict sleep states and apnea-hypopnea index (AHI) without physical contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If wired probes are used to determine sleep states, then measurement precision can be achieved, but device complexity and patient discomfort increase
Solution Approach 1:
The patent replaces mechanical contact-based sensors (wired probes) with optical sensing technology. A camera captures images of the patient's face, and image processing algorithms extract respiratory waveform and sleep state information without physical contact. This substitution eliminates the need for complex wired probe systems while maintaining measurement capability through computational analysis of visual data.
Solution Approach 2:
The patent introduces an intermediary processing layer between the camera and sleep state determination. Image processing algorithms act as intermediaries that transform raw camera images into respiratory waveforms and sleep state classifications. This intermediary computational layer enables precise measurement without direct mechanical contact, resolving the contradiction between precision and device complexity.
2Measurement precision
If wired probes are used for sleep monitoring, then measurement precision is achieved, but ease of operation deteriorates due to patient discomfort
Solution Approach 1:
The patent eliminates mechanical contact-based monitoring by using optical imaging. The camera-based system captures facial images and processes them to determine sleep states without physical contact, thereby maintaining measurement precision while dramatically improving patient comfort and ease of operation during sleep monitoring.
Solution Approach 2:
The system enables self-service monitoring where the patient can sleep naturally without being disturbed by wired probes. The automated image processing and machine learning algorithms perform the monitoring functions autonomously, allowing patients to maintain their natural sleep patterns while still receiving accurate sleep state determination.
3Reliability
If traditional monitoring methods are used, then sleep state data can be collected, but productivity of home monitoring decreases
Solution Approach 1:
The patent replaces complex mechanical monitoring systems with a simplified optical imaging approach. The camera-based system with automated image processing and machine learning algorithms provides reliable sleep monitoring that can be easily implemented in home settings, thereby improving both reliability and productivity of home monitoring.
Solution Approach 2:
The patent changes the fundamental parameters of the monitoring system from mechanical contact-based to optical non-contact based. This parameter change enables the system to maintain clinical reliability while becoming suitable for home monitoring environments, thereby improving accessibility and productivity of sleep monitoring outside clinical settings.
Data Source
AI summary
Implementations described herein disclose a method including determining, based on an image signal received from a camera focused on at least a portion of a patient, a respiratory waveform, receiving an observed sleep signal of the patient temporally corresponding to the respiratory waveform, the observed sleep signal including sleep state labels, labeling various segments of the respiratory waveform using sleep-wake state labels to generate a labeled respiratory waveform, generating an input feature matrix by processing the labeled respiratory waveform, and training a machine learning (ML) model using the input feature matrix.